Multi-die chips, referred to as three-dimensional built-in circuits, or 3D-ICs, symbolize a revolutionary step in semiconductor design. The chips are vertically stacked to create a compact construction that enhances efficiency with out rising energy consumption.
Nonetheless, as chips develop into denser, they current extra advanced challenges in managing electromagnetic and thermal stresses. To know and deal with this, superior 3D multiphysics visualizations develop into important to design and diagnostic processes.
At this week’s Design Automation Convention, a worldwide occasion showcasing the newest developments in chips and methods, Ansys — an organization that develops engineering simulation and 3D design software program — will share the way it’s utilizing NVIDIA know-how to beat these challenges to construct the following technology of semiconductor methods.
To allow 3D visualizations of simulation outcomes for his or her customers, Ansys makes use of NVIDIA Omniverse, a platform of software programming interfaces, software program growth kits, and providers that allows builders to simply combine Common Scene Description (OpenUSD) and NVIDIA RTX rendering applied sciences into current software program instruments and simulation workflows.
The platform powers visualizations of 3D-IC outcomes from Ansys solvers so engineers can consider phenomena like electromagnetic fields and temperature variations to optimize chips for sooner processing, elevated performance and improved reliability.
With Ansys Icepak on the NVIDIA Omniverse platform, engineers can simulate temperatures throughout a chip in accordance with totally different energy profiles and flooring plans. Discovering chip hot-spots can result in higher design of the chips themselves, in addition to auxiliary cooling units. Nonetheless, these 3D-IC simulations are computationally intensive, limiting the variety of simulations and design factors customers can discover.
Utilizing NVIDIA Modulus, mixed with novel strategies for dealing with arbitrary energy patterns within the Ansys RedHawk-SC electrothermal knowledge pipeline and mannequin coaching framework, the Ansys R&D staff is exploring the acceleration of simulation workflows with AI-based surrogate fashions. Modulus is an open-source AI framework for constructing, coaching and fine-tuning physics-ML fashions at scale with a easy Python interface.
With the NVIDIA Modulus Fourier neural operator (FNO) structure, which might parameterize options for a distribution of partial differential equations, Ansys researchers created an AI surrogate mannequin that effectively predicts temperature profiles for any given energy profile and a given flooring plan outlined by system parameters like warmth switch coefficient, thickness and materials properties. This mannequin provides close to real-time outcomes at considerably diminished computational prices, permitting Ansys customers to discover a wider design house for brand spanking new chips.
Following a profitable proof of idea, the Ansys staff will discover integration of such AI surrogate fashions for its next-generation RedHawk-SC platform utilizing NVIDIA Modulus.
As extra surrogate fashions are developed, the staff will even look to boost mannequin generality and accuracy by way of in-situ fine-tuning. This can allow RedHawk-SC customers to learn from sooner simulation workflows, entry to a broader design house and the power to refine fashions with their very own knowledge to foster innovation and security in product growth.
To see the joint demonstration of 3D-IC multiphysics visualization utilizing NVIDIA Omniverse APIs, go to Ansys on the Design Automation Convention, operating June 23-27, in San Francisco at sales space 1308 or watch the presentation on the Exhibitor Discussion board.
